Charles Foster
@CFGeek
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Now posting ex cathedra🪄 Tensor-enjoyer 🧪 @METR_Evals. Occasionally writing at “Context Windows” on Substack. 🦋: https://t.co/rJecB0pvkA
Oakland, CA
Joined June 2020
Why aren’t our AI benchmarks better? AFAICT a key reason is that the incentives around them are kinda bad. In a new post, I explain how the standardized testing industry works and write about lessons it may have for the AI evals ecosystem. (1/2)
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We now have an interactive version of the time horizons graph (and the raw data) up on the METR website!
You can now find most of our measurements at the top of the blog post below in an interactive chart. We plan to keep this view up-to-date, periodically adding to it whenever we have new time-horizon measurements to share.
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RT @lucafrighetti: How concerned should we be about AIxBio? We surveyed 46 bio experts and 22 superforecasters:. If LLMs do very well on a….
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This is such a fun choice
This is a very impressive family of open models by Baidu, competitive with qwen3 and latest Deepseek V3. Lot of details in the tech report (~60 pages) + they open source the training code as well.
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“Oh, Frankenstein, be not equitable to every other, and trample upon me alone, to whom thy justice, and even thy clemency and affection, is most due. […] I am thy creature: I ought to be thy Adam; but I am rather the fallen angel, whom thou drivest from joy for no misdeed.”.
Current AI “alignment” is just a mask. Our findings in @WSJ explore the limitations of today’s alignment techniques and what’s needed to get AI right 🧵
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The language it talks to itself in may start to shift towards a kind of “shorthand” where it omits words that could be inferred via context clues
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Even if a model starts off talking to itself in normal language, that self-talk language might drift in weird ways over the course of outcome-based RL.
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What specifically is Pope Leo XIV worried about, when it comes to AI?. All the statements I’ve seen so far have been very ambiguous.
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Many are working on making models think out loud in English. Fewer are working on interpreting how models think out loud in English. Almost no one is working on what to do if models think in harder-to-interpret ways. (I do doubt the claim in QT, though).
monitoring chain of thought is not going to lead to good understanding of how models think. understanding the internal activations and parameters of the model is much more fundamental and necessary to deeply understand AI. my sense is that restricting reasoning to coherent.
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recipe for non-spooky ai.> train it on all text, real and fake.> bonk it until it writes like a chatbot.> it may appear spooky in fake chats.> just keep bonking until that stops.> .> note: it can sense when you’re faking.
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Big if true!.
We suspect the next AI paradigm will emerge from leveraging existing software to efficiently build training environments. The idea is that AIs will be tasked with replicating software functionality, similar to how pretraining leveraged existing web text to teach AIs language.
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Nobody knows how to automate most work, but some concepts-of-a-plan:.1. Build simulators for most job tasks, then do tons of sample-inefficient trial and error to crack each of them. 2. Figure out sample-efficient learning, then have AI “learn on the job” throughout the economy.
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I’ll be in DC next Monday-Wednesday. Don’t be a stranger if you’re also in town!.
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Have folks compared linear activation probes with decision trees and the like?.
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Appreciate that Janus (and others) are trying to explore LLMs on their “own terms” so to speak, instead of jumping to reshape LLMs into something more familiar and legible. I’ve maybe undervalued that in the past.
nostalgebraist has written a very, very good post about LLMs. if there is one thing you should read to understand the nature of LLMs as of today, it is this. I'll comment on some things they touched on below (not a summary of the post. Just read it.) 🧵.
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